Frequently in medical imaging, a functional image with the contrast of interest has a signal-to-noise ratio (SNR) or spatial resolution lower than ideal. For example, arterial spin labeling (ASL) is a technique in magnetic resonance imaging (MRI) that can be used to provide functional information using MRI, despite the fact that MRI excels at anatomic imaging and does not inherently include functional information in anatomic images. Unfortunately, using ASL, like many other techniques to acquire functional information using MRI, yields a noisy, lower resolution image than may be achieved with images that are intended to be purely anatomic, such as T1-weighted or T2-weighted anatomic images.
As a result, many have sought to improve the quality of functional images acquired using MRI, such as by increasing SNR or compensating for low resolutions. One way to improve the quality of a functional image is to combine the functional image with the anatomic image. In these methods, the acquired functional images are combined with anatomic images in an effort to improve SNR or resolution. These fusion methods are designed to fuse the information in both the anatomic and functional images to maximize the information in the resulting fusion image. The resulting image would include extra structures that may help localize a lesion. But, importantly, this also results in a fusion image of a different contrast. So these “fusion” methods generally produce a combined image with information not readily attributable to either the functional image or the anatomic image, or provide a confusing attempt to overlay both sets of information. As such, the functional information can be obscured or, worse, misleading. Thus, such “fusion” or enhancement methods are often shunned in the clinical environment because clinicians cannot allow the functional data to be obscured or rendered inaccurate.
It would be desirable to have a system and method for enhancing a functional images without interfering or degrading the functional information.